The ecological and evolutionary consequences of systemic racism
Millie Chapman (GSI)
knitr::opts_chunk$set(messages = FALSE, cache = FALSE)
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(sf) # to work with simple features (vector) data## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUElibrary(abind)
library(rstac)
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gdalcubes::gdalcubes_options(parallel = TRUE)#renv::activate()
#renv::snapshot()Learning objectives
This module provides an introduction to the fundamentals of working with spatial vector and raster data in R while empirically exploring why systematic and structural racism is interwined with urban ecological processes. This module uses the Simple Features Access standard (ISO 19125) and tidyverse-style workflow using the sf package and emerging ecosystem of r-spatial tools.
Exercise
In August 2020, Christopher Schell and collegues published a review in Science on ‘The ecological and evolutionary consequences of systemic racism in urban environments’ (DOI: 10.1126/science.aay4497), showing how systematic racism and classism has significant impacts on ecological and evolutionary processes within urban environments. Here we explore a subset of the data used to support these findings in this review and the broader literature.
The press release on the paper is worth a read:
“Racism is destroying our planet, and how we treat each other is essentially structural violence against our natural world,” said lead author Christopher Schell, an assistant professor of urban ecology at the University of Washington Tacoma. “Rather than just changing the conversation about how we treat each other, this paper will hopefully change the conversation about how we treat the natural world.”
In the paper, Schell writes:
“In multiple cases, neighborhood racial composition can be a stronger predictor of urban socio-ecological patterns than wealth.”
We are going to explore one metric for how structural racism and classism underpin landscape heterogeneity in cities.
Figure 2 in the Schell paper shows how NDVI
(Normalized Difference Vegetation Index) tracks historical redlining.
We are going to recreate these city maps, and plot the distributions and mean vegetation patterns across cities to explore the structural inequality and racism that Schell et al highlight in their paper.
To do this we are going to use the following spatial data:
1.Mapping Inequality: (vector data)
Please take the time to read the introduction to this dataset here
sfzip <-"https://dsl.richmond.edu/panorama/redlining/static/downloads/shapefiles/CASanFrancisco1937.zip"
sfurl <- paste0("/vsizip/vsicurl/",sfzip)
sf <- read_sf(sfurl)
sftmap_mode("view")## tmap mode set to interactive viewingtm_shape(sf)+tm_polygons("holc_grade")## STAC Search over 400 million assets.
box <- c(xmin=-122.51006, ymin=37.70801, xmax=-122.36268, ymax=37.80668)
start_date <- "2022-06-01"
end_date <- "2022-08-01"
items <-
stac("https://earth-search.aws.element84.com/v0/") |>
stac_search(collections = "sentinel-s2-l2a-cogs",
bbox = box,
datetime = paste(start_date, end_date, sep="/"),
limit = 100) |>
post_request() col <-
stac_image_collection(items$features,
asset_names = c("B02", "B03", "B04","B08", "SCL"),
property_filter = \(x) {x[["eo:cloud_cover"]] < 20})## Warning in stac_image_collection(items$features, asset_names = c("B02", : STAC
## asset with name 'SCL' does not include eo:bands metadata and will be considered
## as a single band sourcecube <- cube_view(srs = "EPSG:4326",
extent = list(t0 = start_date, t1 = "2022-08-31",
left = box[1], right = box[3],
top = box[4], bottom = box[2]),
nx = 1000, ny = 1000, dt = "P1M",
aggregation = "median", resampling = "average")S2.mask <- image_mask("SCL", values=c(3,8,9)) # mask clouds and cloud shadows
raster_cube(col, cube, mask = S2.mask) |>
select_bands(c("B04", "B03", "B02")) |>
plot(rgb = 1:3)
ndvi <-
raster_cube(col, cube, mask = S2.mask) |>
select_bands(c("B08", "B04")) |>
apply_pixel("(B08-B04)/(B08+B04)", "NDVI") |>
aggregate_time("P3M") |>
st_as_stars()ave_ndvi <-
raster_cube(col, cube, mask = S2.mask) |>
select_bands(c("B08", "B04")) |>
apply_pixel("(B08-B04)/(B08+B04)", "NDVI") |>
aggregate_time("P3M") |>
extract_geom(sf, FUN = mean )
ndvi## stars object with 3 dimensions and 1 attribute
## attribute(s), summary of first 1e+05 cells:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## NDVI -0.6376002 -0.1380293 0.02614136 0.05817926 0.2067806 0.9047929
## dimension(s):
## from to offset delta refsys point values x/y
## x 1 1001 -122.5 0.0001474 WGS 84 NA NULL [x]
## y 1 1000 37.81 -9.867e-05 WGS 84 NA NULL [y]
## time 1 1 NA NA POSIXct FALSE [2022-06-01,2022-09-01)ave_ndvi#ave_ndvi <- ndvi |>
#extract_geom(sf,FUN = mean)“As you explore the materials Mapping Inequality, you will quickly encounter exactly that kind of language, descriptions of the”infiltration” of what were quite often described as “subversive,” “undesirable,” “inharmonious,” or “lower grade” populations, for they are everywhere in the HOLC archive ….These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality.”
2.Normalized Difference Vegetation Index (NDVI) (raster data) NDVI is used as proxy measure of vegetation health, cover and phenology (life cycle stage) over large areas. It is calculated using multiple bands from satellite images.
ndvi2 <- ndvi |>st_as_stars()
tmap_mode("view")## tmap mode set to interactive viewingExercise 1
Create a map which shows current (2019) mean NDVI across city redlining from the 1950s.
tm_shape(ndvi2) + tm_raster(style = "quantile") + tm_shape(sf) + tm_polygons("holc_grade", alpha = 0.5)## stars object downsampled to 1000 by 999 cells. See tm_shape manual (argument raster.downsample)## Variable(s) "NA" contains positive and negative values, so midpoint is set to 0. Set midpoint = NA to show the full spectrum of the color palette.Exercise 2
Plot the average NDVI values in different neighborhoods as well as the distribution of pixel values across cities and neighborhoods. Show how the trends differ between cities.
ave_ndvi |> as_tibble()sf2 <- sf |> rowid_to_column("FID")
sf2ndvi_poly <- left_join(sf2 , ave_ndvi)## Joining with `by = join_by(FID)`ndvi_poly <- filter(ndvi_poly, NDVI > -1)
ndvi_poly <- filter(ndvi_poly, NDVI < 1)
ndvi_polytmap_mode("view")## tmap mode set to interactive viewingtm_basemap() +
tm_shape(ndvi_poly) + tm_polygons("NDVI", palette = "Greens") +
tm_shape(ndvi_poly) + tm_text("holc_grade", size = 0.5)ndvi_poly |> as_tibble() |>
group_by(holc_grade) |>
summarise(mean_NDVI = mean(NDVI))Exercise 3:
Explain why considering systematic inequity and racism is important in the context of global change and developing solutions to the biodiversity crisis.